叙事熵（Sn）的可操作化：双场景注册试点报告与预验证方案
Operationalizing Narrative Entropy (Sn): A Two-Scene Registered Pilot Report and Pre-Validation Protocol
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中文总结 AI 辅助
该研究针对叙事熵（Sn）完成首次可操作化试点，以两部作品的双场景验证公式，发现单视角独白得分更高，预注册了区分差异原因的方案及$I_f$的构念效度测试。
中文摘要 AI 辅助
叙事熵（$S_n$）是Bulut学说中提出的量化描述符，旨在捕捉叙事文本给读者带来的处理负荷速率。迄今为止，该概念仅在理论上被定义，尚未针对真实文本实现可操作化。本报告记录了首次可操作化（v2.0试点）：两个叙事场景——昆汀《落水狗》的开篇餐厅场景，以及卡佛《大教堂》的开篇内心独白段落——由单一评分者手动编码，并采用候选公式$S_n = I_f \times C_b \times t$评分。结果与作者的朴素直觉相悖：单视角独白（$S_n=30.0$）得分高于九角色对话场景（$S_n=18.8$）。我们不将此视为需解释的结果，而将其作为核心发现，拒绝事后调整公式。提出三种竞争性解释——公式不完备、真正的高负荷散文、测量误差——并预注册了用于区分它们的设计。v2.1修订版补充：（i）明确承认该差异与现有架构框架一致，该框架优先推理重构而非表层陈述，v2.0中所谓“与预期相反”反映的是作者的预期直觉，而非方法论自身的预测；（ii）基于$I_f$值在两个场景中近乎相等（1.71 vs 1.58）却导致$S_n$显著差异的观察，为$I_f$添加了预注册的构念效度测试。本文件同时作为试点报告（$n=2$）和下一阶段方案的预注册，不声称$S_n$已被验证。
英文摘要
Narrative Entropy ($S_n$) is a proposed quantitative descriptor within the Bulut Doctrine, intended to capture the rate at which a narrative text imposes processing load on a reader. To date the construct has been defined theoretically but not operationalized against real texts. This report documents the first such operationalization (the v2.0 pilot): two narrative scenes -- the opening restaurant scene of Tarantino's Reservoir Dogs and the opening interior-monologue block of Carver's Cathedral -- were coded manually by a single rater and scored with the candidate formula $S_n = I_f \times C_b \times t$. The result was a divergence from the author's naive intuition: the single-voice monologue ($S_n = 30.0$) scored higher than the nine-character dialogue scene ($S_n = 18.8$). We treat this not as a result to be explained away but as the central finding, and we refuse post-hoc adjustment of the formula. Three competing interpretations are presented -- formula incompleteness, genuine high-load prose, and measurement error -- and the design that would discriminate among them is pre-registered. This v2.1 revision adds: (i) explicit acknowledgement that the divergence is consistent with the pre-existing architectural framework which privileges inferential reconstruction over surface declaration, and that what was called "contrary to expectation" in v2.0 reflected the author's anticipatory intuition rather than the methodology's own predictions; (ii) a pre-registered construct validity test for $I_f$, motivated by the observation that $I_f$ values were nearly equal across the two scenes (1.71 vs 1.58) despite the headline $S_n$ divergence. The document functions simultaneously as a pilot report ($n=2$) and as a pre-registration of the next-stage protocol. It does not claim that $S_n$ has been validated.
